Systems and Methods for Filtering ECG Artifacts

ABSTRACT

Systems and methods of processing raw electrocardiogram (ECG) waveform data of a patient into estimated real-time ECG waveform data. The method includes sensing at least one physical non-cardiac influence on the raw ECG waveform data, constructing a time domain computer model of the at least one physical, non-cardiac influence on the raw ECG waveform data, and adaptively filtering the raw ECG waveform data in the time domain using the constructed time domain computer model of the at least one physical non-cardiac influence on the raw ECG waveform data to form the estimated real-time ECG waveform data. The system can include an ECG device for collecting raw ECG waveform data, at least two ECG electrodes positioned on the patient and electrically coupled to the ECG device, and a processor coupled to the ECG device and configured to compute a time domain model of an artifact created by chest compressions.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application under 35 U.S.C. § 120 andclaims priority to U.S. application Ser. No. 15/212,468, filed Jul. 18,2016, which is a continuation of U.S. application Ser. No. 13/743,891,filed Jan. 17, 2013, now U.S. Pat. No. 9,839,368, which claims priorityunder 35 U.S.C. § 119(e) to U.S. Provisional Application Ser. No.61/587,433,” filed Jan. 17, 2012. The entire contents of eachapplication is incorporated herein by reference in its entirety.

BACKGROUND

According to American Heart Association guidelines, resuscitationtreatments for patients suffering from cardiac arrest generally includeapplying chest compressions to provide blood flow to the victim's heart,brain and other vital organs, clearing and opening the patient's airway,and providing rescue breathing for the patient. The term cardiopulmonaryresuscitation (CPR) refers to an emergency procedure that includes chestcompressions and which may additionally include breathing. Resuscitationtreatments may include establishing a permanent airway throughintubation, with subsequent periodic provision of air to the lungs viapositive pressure support. If the patient has a shockable heart rhythm,as determined by electrocardiogram (ECG) data, resuscitation may alsoinclude defibrillation therapy.

ECG data is generated using multiple electrode pads placed at variouspositions on the patient's body. Electrode pads are often placed on apatient's chest. CPR compressions may cause artifacts to appear on ECGrecordings, making them difficult or impossible to interpret. Thus,rescue workers often stop CPR compressions to obtain accurate ECGrecordings and determine whether defibrillation therapy should beapplied or whether a prior defibrillation attempt was successful.However, studies have shown that stopping CPR compressions may have adetrimental effect on patient survival. Current American HeartAssociation (AHA) protocols for cardiac life support emphasize theimportance of uninterrupted chest compressions. Therefore, interruptionsto assess heart rhythm should be minimized.

SUMMARY

In accordance with one aspect of the present invention, a method ofprocessing raw electrocardiogram (ECG) waveform data of a patient intoestimated real-time ECG waveform data is provided. The method comprisessensing at least one physical non-cardiac influence on the raw ECGwaveform data, constructing a time domain computer model of the at leastone physical, non-cardiac influence on the raw ECG waveform data, andadaptively filtering the raw ECG waveform data in the time domain usingthe constructed time domain computer model of the at least one physicalnon-cardiac influence on the raw ECG waveform data to form the estimatedreal-time ECG waveform data

In accordance with one embodiment, the at least one physical non-cardiacinfluence on the raw ECG waveform is caused by repeated chestcompressions, and constructing a time domain computer model includescollecting several cycles of chest compressions and calculating a chestcompression artifact.

In accordance with another embodiment, adaptively filtering the raw ECGwaveform data includes subtracting the constructed time domain computermodel of the at least one physical non-cardiac influence from the rawECG waveform data.

In accordance with each of the above embodiments, the method may furthercomprise detecting a frequency of repetition of the at least onephysical non-cardiac influence on the raw ECG waveform.

In accordance with another embodiment, sensing at least one physicalnon-cardiac influence on the raw ECG waveform includes sensing the atleast one physical non-cardiac influence on the raw ECG waveform at oneof an ECG electrode, a pad placed beneath the patient, the patient'sskin, an ECG monitoring device, a manually operated chest compressiondevice, and an automatic electro-mechanical chest compression device.

In accordance with another embodiment, sensing at least one physicalnon-cardiac influence on the raw ECG waveform includes detecting the atleast one physical non-cardiac influence on the raw ECG waveform data inthe ECG waveform data.

In accordance with some embodiments, sensing at least one physicalnon-cardiac influence on the raw ECG waveform includes receiving datafrom a chest compression device including at least one of chestcompression rate and chest compression force.

In accordance with any of the above-described embodiments, sensing atleast one physical non-cardiac influence on the raw ECG waveformincludes measuring a change in impedance of an interface between an ECGelectrode and the patient during chest compressions, and constructingthe time domain computer model includes determining an effect of thechange in impedance on the raw ECG waveform data. In accordance with oneaspect of this embodiment, measuring the change in impedance includesmeasuring a change in complex impedances of the interface between theECG electrode and the patient during the chest compressions over a rangeof frequencies lying between 30 kHz and 100 kHz. In accordance withanother aspect of this embodiment, measuring the change in impedanceincludes measuring a change in complex impedances of the interfacebetween the ECG electrode and the patient during the chest compressionsover a range of frequencies lying between 30 kHz and 70 kHz. Inaccordance with a further aspect of this embodiment, measuring thechange in impedance includes measuring a change in complex impedances ofthe interface between the ECG electrode and the patient during the chestcompressions at a frequency of about 35 kHz. In accordance with anotheraspect of this embodiment, measuring the change in impedance includesapplying plural superposed waveforms in a quadrature relationship toeach other to the electrode and computing a complex impedance fromdetected phase, amplitude, and frequency information.

In accordance with another aspect of the present invention, a medicaldevice system is provided. The medical device system, comprises anelectrocardiogram (ECG) device for collecting raw ECG waveform data froma patient, at least two ECG electrodes positioned on the patient andelectrically coupled to the ECG device, and a processor coupled to theECG device and configured to compute a time domain model of an artifactcreated by chest compressions.

In accordance with one embodiment, the medical device system furthercomprises at least one first sensor for detecting the chest compressionsapplied to the patient. In accordance with some embodiments, the medicaldevice system can further comprise a second sensor, where the firstsensor and the second sensor are configured to calculate impedance of aninterface between at least one of the at least two ECG electrodes andthe patient. In accordance with each of these embodiments, the at leastone first sensor is located at one ECG electrode of the at least two ECGelectrodes, a pad placed beneath the patient, the patient's skin, amanually operated chest compression device, and an automaticelectro-mechanical chest compression device.

In accordance with one or more embodiments, the processor is furtherconfigured to detect the artifact based on the raw ECG waveform data. Inaccordance with various embodiments, the medical device system canfurther comprise an automatic electro-mechanical chest compressiondevice configured to apply chest compressions to a patient and toprovide chest compression data to the processor.

In accordance with each of the above described embodiments, the medicaldevice system can further comprise at least one continuous notch filterconfigured to filter the raw ECG waveform and provide a filtered ECGwaveform to the processor, the at least one continuous notch filterincluding at least one first continuous notch filter having a centerfrequency corresponding to a frequency of the chest compressions. Inaccordance with a further embodiment, the at least one continuous notchfilter further includes at least one second continuous notch filterhaving a center frequency corresponding to a first harmonic of thefrequency of the chest compressions.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a schematic diagram of a patient with electrocardiographyelectrode pads, in accordance with one embodiment;

FIG. 2 is a data flow diagram for processing sensor data andelectrocardiography data, in accordance with one embodiment; and

FIG. 3 is a flow chart of a method for processing ECG data, inaccordance with another embodiment.

DETAILED DESCRIPTION

Devices may be used to detect and analyze electrocardiography (ECG)signals acquired from a patient to determine when a cardiac arrhythmiaexists and, if an arrhythmia does exist, whether it is treatable bydefibrillation therapy. However, noise caused by external factors mayinduce artifacts in the ECG data. For example, applying chestcompressions to the patient induces artifacts in the ECG signal. Toobserve a clean and substantially error-free ECG signal and determinewhether defibrillation therapy would be beneficial, a rescue workertemporarily discontinues chest compressions. However, many studies havereported that the discontinuation of chest compressions, such as iscommonly done for ECG analysis, can significantly reduce the recoveryrate of spontaneous circulation and 24-hour survival rate. Therefore,there is a need to assess cardiac rhythm during continuous chestcompressions.

One aspect of the present invention is directed to techniques forfiltering out artifacts caused by chest compressions from an ECG signal,thereby allowing chest compressions to continue while also observing asubstantially error-free ECG signal. In one embodiment, one or morefilters identify and filter out chest compression artifacts. When chestcompression artifacts are identified, a filtering algorithm is used toidentify and/or remove or substantially remove noise induced by thechest compressions from the ECG signal. The filtering algorithm may be atime domain filter. Additionally, the filtering algorithm may beadaptive such that it identifies the different artifacts created bydifferent rescue workers applying chest compressions, or differentartifacts resulting from rescue worker fatigue, and adjusts the filteraccordingly.

In one embodiment, adaptive algorithms may be used to generate a timedomain model of the compression noise. The model may be used to optimizethe ECG waveform during CPR. For example, an inverse noise signal may begenerated using the time domain model of the compression noise and theinverse noise signal may be subtracted from a raw ECG signal at a pointin time and/or space prior to the point in the raw ECG signal where thecompression noise will influence the ECG. In one example, the inversenoise signal is generated using previously recorded raw ECG signal data.The previously recorded raw ECG signal may be the previous few secondsof raw ECG signal recorded from the patient.

A significant source of CPR compression-caused artifacts in an ECGsignal may be due to a charge transfer effect in the electrolyte gelused when attaching the electrode pads to the patient's skin. Themagnitude of the charge transfer effect may depend on how much of eachelectrode pad lies under a compression pad or, in the case of manualcompressions, how much of each electrode pad lies under the compressionsurface. One source of ECG artifacts is the charge transfer effect ofthe electrode pads on ECG measurements. Complex impedance measurementsmay be used to measure, characterize and remove the ECG artifact createdby the interface between the skin and one or more ECG electrode pads. Inparticular, ECG artifacts arise from skin changes due to skin hydrationfrom the electrolyte gel at the location of the electrode pads. As theskin hydrates, electrode pad performance changes. For example, the ECGartifact may decrease over time as the skin hydrates, and the filterremoving the artifact may use complex impedance measurements to identifyand adjust to the decreasing amplitude of the artifact caused by theskin hydrating.

According to one embodiment, complex impedance measurements can includemeasuring the change of impedance by applying plural superposedwaveforms in a quadrature relationship to each other and to theelectrode pads, and computing the complex impedance from detected phase,amplitude, and frequency information. According to one example, compleximpedance is measured over a range of frequencies between about 30 kHzand about 100 kHz. According to another example, complex impedance ismeasured over a range of frequencies between about 30 kHz and about 70kHz. According to further examples, complex impedance is measured overat a frequency of about 30 kHz, about 35 kHz, 40 kHz, 45 kHz, or 50 kHz.

FIG. 1 is a schematic diagram 100 of a patient 102 with ECG electrodepads 104 a-104 b, and an ECG device 106. The ECG device 106 collects ECGdata from the patient using the electrode pads 104 a and 104 b. The ECGdevice 106 may be an Automated External Defibrillator (AED) device,which determines, based on the ECG data, whether the patient 102 wouldbenefit from defibrillation therapy, and if so, can apply adefibrillating shock to the patient. During emergency life supportprocedures, ECG measurements are usually made using two electrode pads104 a and 104 b secured to the patient's chest. However, more than twoelectrode pads 104 a and 104 b may be used, placed at various positionson the patient's body.

When a patient receives CPR, in some instances an automaticelectro-mechanical chest compression device is secured to the patient102 to deliver the chest compressions to the patient 102. In oneexample, the automatic electro-mechanical chest compression device is aZoll Autopulse® cardiac support pump, which includes a board on whichthe patient is placed and a load-distributing band attached to the boardthat squeezes the patient's entire chest to deliver the compressions. Inother instances, when a patient receives CPR, the patient receivesmanual compressions from a rescue worker. The rescue worker may use asmall device while performing chest compressions to measure one or moreof the compression rate, compression depth, and the compressionpressure. In one example, the device is a Zoll PocketCPR® unit that therescue worker places on the patient's chest while performing chestcompressions.

Whether chest compressions are delivered automatically by anelectro-mechanical companion device or manually, one or both of theelectrode pads 104 a-104 b are generally positioned at least partiallyunderneath the automatic electro-mechanical chest compression device orbeneath the hands of the rescue worker providing the chest compressions.The pressure of the chest compressions on the electrode pads 104 a and104 b results in artifacts in the ECG data. A filtering algorithm,described in greater detail with respect to FIGS. 2 and 3, is used toremove or substantially remove the chest compression artifacts from theECG data.

In one embodiment, one or more sensors are used to detect the chestcompressions. The sensors may be pressure sensors, impedance sensors,accelerometers, motion sensors, or any other type of sensor. In variousexamples, the sensors measure chest compression acceleration, chestcompression velocity, chest compression displacement, chest impedanceand chest compression rate. Acceleration, velocity, displacement andcompression rate may be measured using one or more sensors.

Impedance may be measured between two or more sensors. According tovarious examples, the sensors may be located on a pad placed beneath thepatient 102, on the patient 102, on the automatic electro-mechanicalchest compression device, on the hand or arms of a rescue worker, orthey may be secured to the patient's skin. An automaticelectro-mechanical chest compression device may include sensors. Forexample, the Zoll Autopulse® cardiac support pump includes load cellsensors on the board positioned beneath the patient 102. In otherexamples, one or more sensors may be attached to the electrode pads 104a and 104 b.

Data detected by the sensors may be used to generate a model of thechest compression noise that manifests as artifacts in the ECG data,according to an embodiment of the invention. The model of the chestcompression noise may be used to predict or estimate ECG artifacts,which may be filtered out from ECG data. In one example, the filteringalgorithm uses a time domain model to remove the compression artifacts,and the predicted or estimated ECG artifacts are subtracted in the timedomain from the raw ECG data to reduce or substantially eliminate theartifacts from the resulting ECG signal. According to various examples,as discussed above, artifacts may be caused by variations in electrodepad behavior and electrode pad response due to compression of theelectrode pad-skin interface or skin stretching under the electrodepads. In another example, the artifact may be caused by compressioneffects on the patient's underlying tissues and organs.

The filtering algorithm may be used predictively to filter artifactsfrom future ECG data, and it may be adapted just before each compressionto account for the most recent artifact data. The filtering algorithmmay be adapted prior to each compression based upon data from the mostrecent two or more compression cycles. Averaging the ECG data over a fewcompression cycles accentuates the artifacts, which may then be filteredfrom past or future ECG data. In one example, the artifact data from afew compression cycles is used predictively to filter the nextcompression cycle. Previously calculated artifact data may be subtractedfrom the ECG data as it is recorded. In another example, the filteringalgorithm includes ECG data from the current compression cycle, delayingthe filtered ECG output signal. The average of the previous few cyclesmay be repeatedly calculated and the predicted artifact repeatedlyupdated for filtering upcoming cycles of ECG data. In various examples,the filtering algorithm may be adapted based upon data from the mostrecent three, four, five, six, eight, ten, twelve, fifteen, twenty, orall of the previous compression cycles. According to one embodiment, thefiltering algorithm is a time domain filter, created by averaging theECG over a few cycles of artifacts. A time domain filter may beespecially effective at removing artifacts from rhythmic or cyclical ECGdata.

When compressions are provided by an automatic electro-mechanical chestcompression device, the compressions, and the resulting compressionartifacts, are typically consistent. When the compression artifacts aresimilar or consistent, combining artifacts over multiple cycles resultsin a fairly accurate estimated artifact. The artifact waveform for chestcompressions provided by an automatic electro-mechanical compressiondevice resembles an AC-coupled, low-pass filtered square wave. Becauseof the high leading edge of the waveform and the quick falling edge, theload cell signals of electro-mechanical CPR devices can be filteredusing the frequency response of the ECG to produce anartifact-predictive signal. The consistent artifact created byelectro-mechanical compressions may be measured and used to produce anartifact-predictive signal that may be fed forward and subtracted fromthe ECG data in real time. According to another embodiment, themechanical CPR device may provide information on the compression phaseor pressure directly to the ECG device or the device processing the rawECG input data and sensor data is not used.

When compressions are provided manually by a rescue worker, thecompressions may be more variable over time, for example due to rescueworker fatigue. Thus, the filter may use fewer cycles of compressions toestimate the compression artifact created by manual compressions than ituses to estimate the compression artifact created by automaticelectro-mechanical compressions. Furthermore, rescue workers usuallytake turns providing manual compressions. Since each rescue worker mayprovide compressions at a different rate and/or with a different amountof pressure or force, the filter is reset each time a different rescueworker begins providing the compressions. According to one embodiment,for manual compressions, an accelerometer or other sensor may be used togenerate phase data and an artifact-predictive signal to be fed forwardand subtracted from the ECG in real time.

In various examples, the filter may be a time domain filter, a Kalmanfilter, an autoregressive moving average (ARMA) filter, an adaptivenotch filter, or a template-based filter. In one example, a Kalmanfilter may be used to predict the artifact to be subtracted from the ECGdata in a continuously adaptive process. According to one embodiment, aheterodyne process is used to filter out the compression artifact byfiltering the compression artifact in time with a carrier signal. Thecarrier signal is created by a local oscillator and set to be close infrequency to the frequency of the compression rate. The heterodyneprocess may be used to modulate the amplitude of the artifact.

According to one example, a phase-lock loop system is used to demodulatethe compression artifact and remove it. In particular, the phase-lockedloop control system may be used to track the frequency of thecompression artifact and generate an output signal to demodulate it.According to one example, the phase-lock loop system is a circuit thatincludes a variable frequency oscillator and a phase detector. Accordingto one aspect, the circuit compares the phase of the input compressionsignal with the phase of the signal from its output oscillator andadjusts the frequency of its oscillator to keep the phases matched. Afeedback loop is used to control the oscillator frequency based on thesignal detected by the phase detector.

According to one example, as described above, impedance measurementsfrom two or more sensors secured to the patient's skin may be used tomeasure, characterize and remove the effect of the skin-electrode pad104 a-104 b interface on ECG measurements. In one example, one or moreof the impedance-measuring sensors are attached to one of the electrodepads 104 a, and one or more of the impedance-measuring sensors areattached to the other electrode pad 104 b.

According to one aspect, the sensors may be configured to send data to acentral unit. The sensors may send the data through a cable orwirelessly. The central unit may be a computer or a computer processorconnected to the ECG device, which applies one or more filteringalgorithms and provides estimated real-time ECG data. In anotherexample, the central unit or another device may be configured to sendthe sensor data and/or ECG data to a remote system, such as a monitoringstation at a hospital, so that a medical professional who is not withthe patient can monitor the patient's vital signs and/or ECG.

In one embodiment, the sensors may be attached to or integrated with amedical device, such as a cardiac support pump, CPR assist device orchest compression device. These sensors may be configured to detectartifacts caused by the medical device or by a rescuer who is applyingchest compressions manually.

In one embodiment, the sensors are coupled to one or more transmittersconfigured to transmit data generated by the sensors to a centralprocessing device, such an automatic emergency defibrillator (AED) orother device that analyzes ECG waveforms. The central processing devicemay be a device travelling with the patient or a remote device, such asa monitoring station at a hospital or doctor's office. The transmittersmay be wireless transmitters, or the transmitters may transmit datathrough a cable or other wired-based interfaces. Various conventionalwireless technologies may be used, including wireless USB, WiFi™,Bluetooth®, and Zigbee®. Other, more long range, technologies may alsobe used, such as CDMA, GSM, 3G or 4G. The protocol used to transmit thedata may be configured to preserve phase (time) information associatedwith the noise being measured so as to enable the various signals to betime-correlated.

FIG. 2 shows a data flow diagram 200 for processing ECG data, and,optionally, sensor data, according to one embodiment. The processingsystem 220 is configured to compute a time-domain model 222 of aninfluence of the sensor data on raw ECG waveform data 230. In someembodiments, the processing system 220 detects artifacts in the ECGsignal without using sensor data. For example, using a time domainfilter model, the processing system 220 may identify cyclically repeatednoise, from which it can determine the compression rate and aggregatetwo or more cycles of artifacts to amplify the artifact noise and filterit from the ECG signal. In another example, the processing system 220may receive data directly from an automatic electro-mechanical chestcompression device indicating the rate and force at which it is applyingchest compressions.

Sensor data 210 from one or more sensors may be received by theprocessing system 220, such as a system having a general purpose orspecial purpose processor. In the case where sensor data 210 is receivedfrom more than one sensor, the processing system 220 is configured tocompute the time domain model 222 of the data of one sensor relative todata from one or more other sensors. In one example, if each sensor issensing at a different location, then the time-domain model 222 willreflect the combined or cumulative effects of the data sensed at eachdifferent location. For example, noise induced into an ECG signal of thepatient 102 may result from compressions, and depending on the type ofsensor (e.g., a pressure sensor, accelerometer, impedance sensor) anddepending on oscillation phases, the sensor data may have an additive orsubtractive effect on other sensor data and on the ECG at any givenpoint in time. In another example, two or more phases of a signal from aparticular sensor may be added together. Accordingly, the time domainmodel 222 may be adapted to account for compression effects by comparingthe sensor data 210 from each sensor to identify noise componentsemanating from each sensor.

In one embodiment, the time domain model 222 is used to estimate atleast one artifact 240 that results from the influence of compressionson the raw ECG data 230. Sensor data 210 may provide a fiducial pointrelating to each compression, allowing the time domain model 222 toalign the cycles and add the ECG data, thereby accentuating thecompression artifact. The estimated artifact 240 may be added to,subtracted from, or combined with the raw ECG data 230 using an adder250 or other appropriate technique to provide estimated ECG data 260.The estimated ECG data 260 represents the raw ECG data 230 without orsubstantially without the artifacts.

In some embodiments, the processing system 220 detects artifacts in theECG signal without using sensor data. For example, the processing system220 may be able to determine the frequency of compression cycles fromprominent ECG artifacts, and use a time domain model 222 to align two ormore compression cycles to accentuate the compression artifact. Asdescribed above, the estimated artifact 240 may be added to, subtractedfrom, or otherwise combined with the raw ECG data 230 using an adder 250or other appropriate technique to provide estimated ECG data 260.

According to another embodiment, the time-domain model 222 is used toestimate at least one artifact created by the skin-to-electrodeinterface described above. The artifact created by the skin-to-electrodeinterface may be determined using complex impedance measurements fromtwo or more sensors positioned on the patient's skin. The estimatedartifact 240 may be added to, subtracted from, or combined with the rawECG data 230 using an adder 250 or other appropriate technique toprovide estimated ECG data 260. The estimated ECG data 260 may, forexample, represent the raw ECG data 230 without or substantially withoutthe artifacts.

In one embodiment, a clock 270 may be used by the processing system 220to correlate, or otherwise compare and combine, the sensor data 210 fromseveral different sensors on the patient in real time. The clock signalmay also be shared with each of the sensors to provide a commontimeframe. The data generated by each sensor may, in one embodiment, beautomatically correlated (e.g., in time) or otherwise compared andcombined. The combined or correlated data may be analyzed to identifysignificant noise components from one or more locations on the patient,including compression noise.

Such identified noise components may be used for a number of purposes.One purpose is to filter out the noise component and provide estimatedreal-time ECG data that may be used by the rescue worker withoutrequiring the rescue worker to stop compressions in order to view usableECG data. Another purpose may include notifying the rescuer that the ECGdata is less reliable or unreliable because of the noise.

In one embodiment, an interdigitated electrode configuration can be usedto measure raw ECG waveforms (including artifacts) and impedancevariations causing artifacts substantially independently of each other.According to one embodiment, impedance measurements include measuringthe change of impedance by applying plural superposed waveforms in aquadrature relationship to each other to the electrode, and computingthe complex impedance from phase, amplitude, and frequency informationdetected. This information may be used in combination with other sensorinformation to identify artifacts in the ECG. In one example, the othersensor information may be data from one or more other sensors. Accordingto a further embodiment, the artifacts may be removed in real-time usingestimation techniques based on the real-time modeling of thecompression-induced noise in the ECG signal.

FIG. 3 is a flow chart of a method 300 for processing raw ECG data andproducing estimated real-time ECG data, according to another embodimentof the invention. At step 302, a physical non-cardiac influence on theraw ECG waveform data is sensed at a first location. At step 304, atime-domain computer model of the at least one physical, non-cardiacinfluence on the raw ECG waveform data is constructed. At step 306, theraw ECG waveform data is adaptively filtered, in the time domain, usingthe constructed time domain computer model of the physical, non-cardiacinfluence on the raw ECG waveform data to form the estimated real-timeECG waveform data.

According to another embodiment, in contrast to the adaptivenon-continuous filters described above, a continuous filter may be usedto filter out artifacts from the ECG data. The continuous filter may besynchronized to the chest compressions. In one example, one or morenotch filters are used to filter out artifacts from ECG data. Whenmultiple notch filters are used to remove the compression artifacts, thecenter frequency of the notch filter can correspond to the chestcompression rate or two or more times that chest compression rate (e.g.,harmonics of the chest compression rate). The chest compression rate maybe determined using sensor data (including, but not limited to pressuresensors, impedance sensors, accelerometers, motion sensors, or othertypes of sensors), data provided by an automatic electro-mechanicalchest compression device, or data derived from a calculation ofcompression rate based on the incoming raw ECG data. In one example, twofilters are set to the compression frequency, two filters are set to thefirst harmonic of the compression frequency (where the first harmonic isequal to two times the compression frequency), and one filter is set tothe second harmonic of the compression frequency (where the secondharmonic is equal to three times the compression frequency). Optionally,additional filters may be used to filter out higher harmonics of thecompression frequency. In another example, the center frequency of thenotch filter corresponds to the most prominent frequency of theartifact.

Having thus described several aspects of at least one embodiment of theinvention, it is to be appreciated various alterations, modifications,and improvements will readily occur to those skilled in the art. Suchalterations, modifications, and improvements are intended to be part ofthis disclosure, and are intended to be within the scope of theinvention. Accordingly, the foregoing description and drawings are byway of example only.

What is claimed is: 1.-20. (canceled)
 21. A medical system formonitoring an electrocardiogram (ECG) of a patient, the systemcomprising: a plurality of electrodes configured to generate signalsindicative of the ECG of the patient; a chest compression sensorconfigured to generate signals indicative of chest compressions appliedto the patient; and one or more processors communicatively coupled withthe plurality of electrodes and the chest compression sensor andconfigured to perform operations comprising: receiving the signalsindicative of the ECG of the patient, receiving the signals indicativeof chest compressions applied to the patient, estimating a frequency ofthe chest compressions applied to the patient, and generating a filteredECG based on the signals indicative of the ECG of the patient via atleast one notch filter having a center frequency corresponding to theestimated frequency of the chest compressions.
 22. The system of claim21, wherein the center frequency is substantially equal to the estimatedfrequency of the chest compressions.
 23. The system of claim 21, whereinthe center frequency comprises a harmonic of the estimated frequency ofthe chest compressions.
 24. The system of claim 23, wherein the centerfrequency comprises a first harmonic of the estimated frequency of thechest compressions.
 25. The system of claim 23, wherein the centerfrequency comprises a second harmonic of the estimated frequency of thechest compressions.
 26. The system of claim 21, wherein the centerfrequency comprises a first frequency and the at least one notch filtercomprises a first notch filter having the first center frequency and theat least one notch filter further comprises a second notch filter havinga second center frequency based on the estimated frequency of the chestcompressions, the second center frequency being different from the firstcenter frequency.
 27. The system of claim 26, wherein the second centerfrequency comprises a first harmonic of the estimated frequency ofcompressions.
 28. The system of claim 26, wherein the at least one notchfilter comprises a third notch filter having a third center frequencybased on the estimated frequency of the chest compressions, the thirdcenter frequency being different from the first center frequency and thesecond center frequency.
 29. The system of claim 28, wherein the thirdcenter frequency comprises a second harmonic of the estimated frequencyof compressions.
 30. The system of claim 21, wherein the at least onenotch filter comprises an adaptive notch filter.
 31. The system of claim21, wherein the at least one notch filter comprises a continuous notchfilter.
 32. The system of claim 21, wherein the chest compression sensorcomprises at least one of a pressure sensor, an impedance sensor, anaccelerometer, and a motion sensor.
 33. The system of claim 21, whereinthe chest compression sensor is integrated in at least one of an ECGelectrode, a pad placed beneath the patient, a pad placed on thepatient's skin, a pad attached to the patient's skin, an ECG monitoringdevice, a manually operated chest compression device, and an automaticelectro-mechanical chest compression device.
 34. The system of claim 33,comprising a monitor configured to display the filtered ECG.
 35. Thesystem of claim 33, comprising a defibrillation circuitry includingelectrodes configured to provide defibrillation therapy.
 36. The systemof claim 21, wherein the estimated frequency is between about 30 kHz andabout 100 kHz.
 37. The system of claim 21, wherein the chestcompressions applied to the patient are manually applied.
 38. The systemof claim 21, wherein the chest compressions applied to the patient areprovided by an automated chest compression device.
 39. A non-transitorycomputer readable storage device storing instructions executable by acomputing device to carry out operations for monitoring anelectrocardiogram (ECG) of a patient, the operations comprising:receiving signals indicative of the ECG of the patient from a pluralityof electrodes; receiving the signals indicative of chest compressionsapplied to the patient from a chest compression sensor; estimating afrequency of the chest compressions applied to the patient; andgenerating a filtered ECG based on the signals indicative of the ECG ofthe patient via at least one notch filter having a center frequencycorresponding to the estimated frequency of the chest compressions. 40.The non-transitory computer readable storage device of claim 39, whereinthe center frequency is substantially equal to the estimated frequencyof the chest compressions.
 41. The non-transitory computer readablestorage device of claim 39, wherein the center frequency comprises aharmonic of the estimated frequency of the chest compressions.
 42. Thenon-transitory computer readable storage device of claim 41, wherein thecenter frequency comprises a first harmonic of the estimated frequencyof the chest compressions.
 43. The non-transitory computer readablestorage device of claim 41, wherein the center frequency comprises asecond harmonic of the estimated frequency of the chest compressions.44. The non-transitory computer readable storage device of claim 39,wherein the center frequency comprises a first frequency and the atleast one notch filter comprises a first notch filter having the firstcenter frequency and the at least one notch filter further comprises asecond notch filter having a second center frequency based on theestimated frequency of the chest compressions, the second centerfrequency being different from the first center frequency.
 45. Thenon-transitory computer readable storage device of claim 44, wherein thesecond center frequency comprises a first harmonic of the estimatedfrequency of compressions.
 46. The non-transitory computer readablestorage device of claim 44, wherein the at least one notch filtercomprises a third notch filter having a third center frequency based onthe estimated frequency of the chest compressions, the third centerfrequency being different from the first center frequency and the secondcenter frequency.
 47. The non-transitory computer readable storagedevice of claim 46, wherein the third center frequency comprises asecond harmonic of the estimated frequency of compressions.
 48. Thenon-transitory computer readable storage device of claim 39, wherein theat least one notch filter comprises an adaptive notch filter.
 49. Thenon-transitory computer readable storage device of claim 39, wherein theat least one notch filter comprises a continuous notch filter.
 50. Thenon-transitory computer readable storage device of claim 39, wherein theestimated frequency is between about 30 kHz and about 100 kHz.
 51. Acomputer-implemented method for monitoring an electrocardiogram (ECG) ofa patient, the method being executed by one or more processors andcomprising: receiving, by the one or more processors, signals indicativeof the ECG of the patient from a plurality of electrodes; receiving, bythe one or more processors, the signals indicative of chest compressionsapplied to the patient from a chest compression sensor; estimating, bythe one or more processors, a frequency of the chest compressionsapplied to the patient; and generating, by the one or more processors, afiltered ECG based on the signals indicative of the ECG of the patientvia at least one notch filter having a center frequency corresponding tothe estimated frequency of the chest compressions.
 52. The method ofclaim 51, wherein the center frequency is substantially equal to theestimated frequency of the chest compressions.
 53. The method of claim51, wherein the center frequency comprises a harmonic of the estimatedfrequency of the chest compressions.
 54. The method of claim 53, whereinthe center frequency comprises a first harmonic of the estimatedfrequency of the chest compressions.
 55. The method of claim 53, whereinthe center frequency comprises a second harmonic of the estimatedfrequency of the chest compressions.
 56. The method of claim 51, whereinthe center frequency comprises a first frequency and the at least onenotch filter comprises a first notch filter having the first centerfrequency and the at least one notch filter further comprises a secondnotch filter having a second center frequency based on the estimatedfrequency of the chest compressions, the second center frequency beingdifferent from the first center frequency.
 57. The method of claim 56,wherein the second center frequency comprises a first harmonic of theestimated frequency of compressions.
 58. The method of claim 56, whereinthe at least one notch filter comprises a third notch filter having athird center frequency based on the estimated frequency of the chestcompressions, the third center frequency being different from the firstcenter frequency and the second center frequency.
 59. The method ofclaim 58, wherein the third center frequency comprises a second harmonicof the estimated frequency of compressions.
 60. The method of claim 51,wherein the at least one notch filter comprises an adaptive notchfilter.
 61. The method of claim 51, wherein the at least one notchfilter comprises a continuous notch filter.
 62. The method of claim 51,wherein the estimated frequency is between about 30 kHz and about 100kHz.